Urban Dataset Meta-Data Maturity Model

Introduction: With the surge in urban data availability, the dataset retrieval challenge for urban studies has increased, often due to poor meta-data and data localization. We address these issues by proposing an urban dataset meta-data maturity model and framework that enhances meta-data management vital for data librarians and curators. We aim to establish a meta-data standard that simplifies finding datasets, catering to various stakeholders—governmental, commercial, civil, and philanthropic—and supporting global data sharing among urban data managers and repositories.

Methodology: Leveraging existing meta-data standards like DCAT, Schema.org, DQV, and the FAIR principles, the proposed standard introduces a dataset meta-data maturity model categorized into seven distinct categories. Following Fox et al. (2024), the model provides a framework for defining and representing the maturity of a dataset’s meta-data, where higher maturity denotes greater detail, focusing on attributes that facilitate searching by topic, spatial and temporal aspects of datasets. Other levels focus on licensing, governance, adherence to FAIR principles, privacy and quality issues. The model integrates linked-data standards and enhances meta-data analysis by transforming meta-data into a knowledge graph. We evaluate the model by statistical analysis of entries in a catalogue of urban datasets.

The model comprises six levels of meta-data attributes: Level 1: Basic information often used in dataset searches, including descriptions and temporal/geospatial data, for easy retrieval and identification. Level 2: Dataset access information, including location, licensing, and points of contact. Level 3: Additional documentation and access meta-data, alignment with FAIR principles for improved identification and interoperability. Level 4: Privacy matters, including individualized (vs aggregate) data and Indigenous data collection standards. Level 5: FAIR principles, ensuring findability, accessibility, interoperability, and retrievability. Level 6: Statistical and quality meta-data, focusing on completeness and accuracy.

Fox, M., Gajderowicz ,B., Lyu, D. (2024), A Maturity Model for Urban Dataset Meta-data. Manuscript under review.